Direct Data Driven Control Using Noisy Measurements

• URL: http://arxiv.org/abs/2505.06407v1
• Date: Fri, 09 May 2025 20:09:44 GMT
• Title: Direct Data Driven Control Using Noisy Measurements
• Authors: Ramin Esmzad, Gokul S. Sankar, Teawon Han, Hamidreza Modares,
• Abstract summary: This paper presents a novel direct data-driven control framework for solving the linear quadratic regulator (LQR) under disturbances and noisy state measurements.<n>Our approach guarantees mean-square stability (MSS) and optimal performance by leveraging convex optimization techniques.<n>The proposed framework offers a practical and theoretically grounded solution for controller design in noise-corrupted environments.
• Score: 6.157271985036265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel direct data-driven control framework for solving the linear quadratic regulator (LQR) under disturbances and noisy state measurements. The system dynamics are assumed unknown, and the LQR solution is learned using only a single trajectory of noisy input-output data while bypassing system identification. Our approach guarantees mean-square stability (MSS) and optimal performance by leveraging convex optimization techniques that incorporate noise statistics directly into the controller synthesis. First, we establish a theoretical result showing that the MSS of an uncertain data-driven system implies the MSS of the true closed-loop system. Building on this, we develop a robust stability condition using linear matrix inequalities (LMIs) that yields a stabilizing controller gain from noisy measurements. Finally, we formulate a data-driven LQR problem as a semidefinite program (SDP) that computes an optimal gain, minimizing the steady-state covariance. Extensive simulations on benchmark systems -- including a rotary inverted pendulum and an active suspension system -- demonstrate the superior robustness and accuracy of our method compared to existing data-driven LQR approaches. The proposed framework offers a practical and theoretically grounded solution for controller design in noise-corrupted environments where system identification is infeasible.

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